{
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  "name": "",
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Final Analysis"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import statsmodels.formula.api as smf\n",
      "from scipy import stats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Import Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "FFM medical landscapre\n",
      "\"\"\"\n",
      "ffmPlans = pd.read_csv('data/2015_QHP_Landscape_Individual_Market_Medical.csv')\n",
      "ffmPlanIds = set(ffmPlans['Plan ID (standard component)'])\n",
      "\n",
      "\"\"\"\n",
      "2015 Unified Rate Review\n",
      "\"\"\"\n",
      "urr = pd.read_csv('data/URR_2015.csv')\n",
      "urr = urr[(urr['Exchange Plan']==1)&(urr['Market']=='Individual')]\n",
      "\n",
      "\"\"\"\n",
      "2014 Unified Rate Review\n",
      "\"\"\"\n",
      "urr14 = pd.read_csv('data/URR_2014.csv')\n",
      "prev = set(urr['Plan ID (Standard Component ID)'])&ffmPlanIds&set(urr14['Plan ID (Standard Component ID)'])\n",
      "urr14 = urr14[urr14['Plan ID (Standard Component ID)'].map(lambda p:p in prev)].sort('Plan ID (Standard Component ID)').reset_index()\n",
      "\n",
      "\"\"\"\n",
      "Get overlap between 2015 and 2014 URR (data wrangling step 1 in paper)\n",
      "\"\"\"\n",
      "sub = urr[urr['Plan ID (Standard Component ID)'].map(lambda p:p in prev)].sort('Plan ID (Standard Component ID)').reset_index()\n",
      "assert len(urr14)==len(sub)\n",
      "assert all(sub['Plan ID (Standard Component ID)']==urr14['Plan ID (Standard Component ID)'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "//anaconda/lib/python2.7/site-packages/pandas/io/parsers.py:1170: DtypeWarning: Columns (17,18) have mixed types. Specify dtype option on import or set low_memory=False.\n",
        "  data = self._reader.read(nrows)\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Why Index Rate for Projection Period is best measure of Issuer per State cost"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Table 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Regression analysis to show the predictive power of index rate on final premiums shown to consumers\n",
      "\"\"\"\n",
      "overlap = set(ffmPlans['Plan ID (standard component)'])&set(urr['Plan ID (Standard Component ID)'])\n",
      "\n",
      "ffmPlans = ffmPlans[ffmPlans['Plan ID (standard component)'].map(lambda p:p in overlap)]\n",
      "ffmPlans['Premium'] = ffmPlans['Premium Adult Individual Age 21'].map(lambda x:float(str(x).replace('$','').replace(',','')))\n",
      "ffmPlans['Deductible'] = ffmPlans['Medical Deductible-individual-standard'].map(lambda x:float(str(x).replace('$','').replace(',','')))\n",
      "ffmPlans['Moop'] = ffmPlans['Medical Maximum Out Of Pocket - individual - standard'].map(lambda x:float(str(x).replace('$','').replace(',','')))\n",
      "ffmPlans=ffmPlans.rename(columns = {'Plan Type':'PlanNetworkType','Metal Level':'Metal','Rating Area':'RatingArea','Issuer Name':'Issuer','Plan ID (standard component)':'PlanId'})\n",
      "\n",
      "lookup = urr.set_index('Plan ID (Standard Component ID)')['Index Rate for Projection Period'].to_dict()\n",
      "ffmPlans['ProjectedIndexRate'] = ffmPlans.PlanId.map(lambda x:lookup[x] if x in lookup else np.nan)\n",
      "lookup = urr.set_index('Plan ID (Standard Component ID)')['AV Metal Value'].to_dict()\n",
      "ffmPlans['AvMetal'] = ffmPlans.PlanId.map(lambda x:lookup[x] if x in lookup else np.nan)\n",
      "lookup = urr.set_index('Plan ID (Standard Component ID)')['Plan Section 4 - Plan Adjusted Index Rate'].to_dict()\n",
      "ffmPlans['ProjectedPlanAdjustedIndexRate'] = ffmPlans.PlanId.map(lambda x:lookup[x] if x in lookup else np.nan)\n",
      "\n",
      "ffmPlans = ffmPlans.dropna(subset=['ProjectedIndexRate'])\n",
      "ffmPlans['StateRatingArea'] = ffmPlans.State+ffmPlans.RatingArea\n",
      "\n",
      "est = smf.ols(formula='Premium ~ ProjectedIndexRate+C(Metal)+PlanNetworkType+C(State)', data=ffmPlans).fit()\n",
      "est.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>         <td>Premium</td>     <th>  R-squared:         </th>  <td>   0.765</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.765</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   7594.</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Fri, 17 Jul 2015</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>08:20:32</td>     <th>  Log-Likelihood:    </th> <td>-4.6950e+05</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td> 95546</td>      <th>  AIC:               </th>  <td>9.391e+05</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td> 95504</td>      <th>  BIC:               </th>  <td>9.395e+05</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>    41</td>      <th>                     </th>      <td> </td>     \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "              <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Intercept</th>                <td>  148.7303</td> <td>    2.143</td> <td>   69.412</td> <td> 0.000</td> <td>  144.531   152.930</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(Metal)[T.Catastrophic]</th> <td>  -36.0088</td> <td>    0.493</td> <td>  -73.057</td> <td> 0.000</td> <td>  -36.975   -35.043</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(Metal)[T.Gold]</th>         <td>   95.3292</td> <td>    0.298</td> <td>  319.430</td> <td> 0.000</td> <td>   94.744    95.914</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(Metal)[T.Platinum]</th>     <td>  157.7984</td> <td>    0.543</td> <td>  290.679</td> <td> 0.000</td> <td>  156.734   158.862</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(Metal)[T.Silver]</th>       <td>   46.8603</td> <td>    0.265</td> <td>  176.535</td> <td> 0.000</td> <td>   46.340    47.381</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>PlanNetworkType[T.HMO]</th>   <td>   -6.8747</td> <td>    0.639</td> <td>  -10.759</td> <td> 0.000</td> <td>   -8.127    -5.622</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>PlanNetworkType[T.POS]</th>   <td>    4.9553</td> <td>    0.727</td> <td>    6.821</td> <td> 0.000</td> <td>    3.531     6.379</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>PlanNetworkType[T.PPO]</th>   <td>   25.1099</td> <td>    0.660</td> <td>   38.062</td> <td> 0.000</td> <td>   23.817    26.403</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.AL]</th>           <td>  -70.6042</td> <td>    1.850</td> <td>  -38.165</td> <td> 0.000</td> <td>  -74.230   -66.978</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.AR]</th>           <td>  -59.6816</td> <td>    1.721</td> <td>  -34.674</td> <td> 0.000</td> <td>  -63.055   -56.308</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.AZ]</th>           <td>  -46.9921</td> <td>    1.891</td> <td>  -24.848</td> <td> 0.000</td> <td>  -50.699   -43.285</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.DE]</th>           <td>  -43.8327</td> <td>    4.109</td> <td>  -10.667</td> <td> 0.000</td> <td>  -51.887   -35.778</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.FL]</th>           <td>  -22.2946</td> <td>    1.634</td> <td>  -13.644</td> <td> 0.000</td> <td>  -25.497   -19.092</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.GA]</th>           <td>  -22.9741</td> <td>    1.619</td> <td>  -14.186</td> <td> 0.000</td> <td>  -26.148   -19.800</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.IA]</th>           <td>  -42.3610</td> <td>    1.931</td> <td>  -21.934</td> <td> 0.000</td> <td>  -46.146   -38.576</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.IL]</th>           <td>  -46.3670</td> <td>    1.675</td> <td>  -27.675</td> <td> 0.000</td> <td>  -49.651   -43.083</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.IN]</th>           <td>  -10.1865</td> <td>    1.553</td> <td>   -6.560</td> <td> 0.000</td> <td>  -13.230    -7.143</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.KS]</th>           <td>  -95.3133</td> <td>    1.747</td> <td>  -54.549</td> <td> 0.000</td> <td>  -98.738   -91.889</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.LA]</th>           <td>  -31.0296</td> <td>    1.607</td> <td>  -19.314</td> <td> 0.000</td> <td>  -34.178   -27.881</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.ME]</th>           <td>  -21.2795</td> <td>    2.152</td> <td>   -9.888</td> <td> 0.000</td> <td>  -25.498   -17.061</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.MI]</th>           <td>  -42.4392</td> <td>    1.622</td> <td>  -26.167</td> <td> 0.000</td> <td>  -45.618   -39.260</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.MO]</th>           <td>  -31.6473</td> <td>    1.744</td> <td>  -18.150</td> <td> 0.000</td> <td>  -35.065   -28.230</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.MS]</th>           <td>  -34.3088</td> <td>    1.727</td> <td>  -19.863</td> <td> 0.000</td> <td>  -37.694   -30.923</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.MT]</th>           <td>  -73.3897</td> <td>    1.784</td> <td>  -41.132</td> <td> 0.000</td> <td>  -76.887   -69.893</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.NC]</th>           <td>  -30.5965</td> <td>    1.615</td> <td>  -18.946</td> <td> 0.000</td> <td>  -33.762   -27.431</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.ND]</th>           <td>  -45.3742</td> <td>    1.784</td> <td>  -25.429</td> <td> 0.000</td> <td>  -48.872   -41.877</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.NE]</th>           <td>  -33.6294</td> <td>    1.702</td> <td>  -19.762</td> <td> 0.000</td> <td>  -36.965   -30.294</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.NH]</th>           <td>  -35.2685</td> <td>    2.242</td> <td>  -15.730</td> <td> 0.000</td> <td>  -39.663   -30.874</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.NJ]</th>           <td>   -3.2433</td> <td>    1.789</td> <td>   -1.813</td> <td> 0.070</td> <td>   -6.750     0.264</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.OH]</th>           <td>  -35.1388</td> <td>    1.616</td> <td>  -21.749</td> <td> 0.000</td> <td>  -38.305   -31.972</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.OK]</th>           <td>  -70.7722</td> <td>    1.761</td> <td>  -40.186</td> <td> 0.000</td> <td>  -74.224   -67.320</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.PA]</th>           <td>  -77.5729</td> <td>    1.647</td> <td>  -47.086</td> <td> 0.000</td> <td>  -80.802   -74.344</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.SC]</th>           <td>  -34.8191</td> <td>    1.685</td> <td>  -20.663</td> <td> 0.000</td> <td>  -38.122   -31.516</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.SD]</th>           <td>  -46.4702</td> <td>    1.733</td> <td>  -26.818</td> <td> 0.000</td> <td>  -49.866   -43.074</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.TN]</th>           <td>  -67.0819</td> <td>    1.724</td> <td>  -38.904</td> <td> 0.000</td> <td>  -70.462   -63.702</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.TX]</th>           <td>  -42.1135</td> <td>    1.643</td> <td>  -25.633</td> <td> 0.000</td> <td>  -45.334   -38.893</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.UT]</th>           <td>  -74.4249</td> <td>    1.840</td> <td>  -40.457</td> <td> 0.000</td> <td>  -78.031   -70.819</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.VA]</th>           <td>  -41.4292</td> <td>    1.641</td> <td>  -25.251</td> <td> 0.000</td> <td>  -44.645   -38.213</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.WI]</th>           <td>  -24.9885</td> <td>    1.496</td> <td>  -16.707</td> <td> 0.000</td> <td>  -27.920   -22.057</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.WV]</th>           <td>  -66.9652</td> <td>    1.962</td> <td>  -34.137</td> <td> 0.000</td> <td>  -70.810   -63.120</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>C(State)[T.WY]</th>           <td>   17.9853</td> <td>    1.682</td> <td>   10.696</td> <td> 0.000</td> <td>   14.689    21.281</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>ProjectedIndexRate</th>       <td>    0.1872</td> <td>    0.002</td> <td>  118.962</td> <td> 0.000</td> <td>    0.184     0.190</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
        "  <th>Omnibus:</th>       <td>12134.993</td> <th>  Durbin-Watson:     </th> <td>   0.575</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>29738.633</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Skew:</th>           <td> 0.738</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Kurtosis:</th>       <td> 5.301</td>   <th>  Cond. No.          </th> <td>4.16e+04</td> \n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                Premium   R-squared:                       0.765\n",
        "Model:                            OLS   Adj. R-squared:                  0.765\n",
        "Method:                 Least Squares   F-statistic:                     7594.\n",
        "Date:                Fri, 17 Jul 2015   Prob (F-statistic):               0.00\n",
        "Time:                        08:20:32   Log-Likelihood:            -4.6950e+05\n",
        "No. Observations:               95546   AIC:                         9.391e+05\n",
        "Df Residuals:                   95504   BIC:                         9.395e+05\n",
        "Df Model:                          41                                         \n",
        "============================================================================================\n",
        "                               coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "--------------------------------------------------------------------------------------------\n",
        "Intercept                  148.7303      2.143     69.412      0.000       144.531   152.930\n",
        "C(Metal)[T.Catastrophic]   -36.0088      0.493    -73.057      0.000       -36.975   -35.043\n",
        "C(Metal)[T.Gold]            95.3292      0.298    319.430      0.000        94.744    95.914\n",
        "C(Metal)[T.Platinum]       157.7984      0.543    290.679      0.000       156.734   158.862\n",
        "C(Metal)[T.Silver]          46.8603      0.265    176.535      0.000        46.340    47.381\n",
        "PlanNetworkType[T.HMO]      -6.8747      0.639    -10.759      0.000        -8.127    -5.622\n",
        "PlanNetworkType[T.POS]       4.9553      0.727      6.821      0.000         3.531     6.379\n",
        "PlanNetworkType[T.PPO]      25.1099      0.660     38.062      0.000        23.817    26.403\n",
        "C(State)[T.AL]             -70.6042      1.850    -38.165      0.000       -74.230   -66.978\n",
        "C(State)[T.AR]             -59.6816      1.721    -34.674      0.000       -63.055   -56.308\n",
        "C(State)[T.AZ]             -46.9921      1.891    -24.848      0.000       -50.699   -43.285\n",
        "C(State)[T.DE]             -43.8327      4.109    -10.667      0.000       -51.887   -35.778\n",
        "C(State)[T.FL]             -22.2946      1.634    -13.644      0.000       -25.497   -19.092\n",
        "C(State)[T.GA]             -22.9741      1.619    -14.186      0.000       -26.148   -19.800\n",
        "C(State)[T.IA]             -42.3610      1.931    -21.934      0.000       -46.146   -38.576\n",
        "C(State)[T.IL]             -46.3670      1.675    -27.675      0.000       -49.651   -43.083\n",
        "C(State)[T.IN]             -10.1865      1.553     -6.560      0.000       -13.230    -7.143\n",
        "C(State)[T.KS]             -95.3133      1.747    -54.549      0.000       -98.738   -91.889\n",
        "C(State)[T.LA]             -31.0296      1.607    -19.314      0.000       -34.178   -27.881\n",
        "C(State)[T.ME]             -21.2795      2.152     -9.888      0.000       -25.498   -17.061\n",
        "C(State)[T.MI]             -42.4392      1.622    -26.167      0.000       -45.618   -39.260\n",
        "C(State)[T.MO]             -31.6473      1.744    -18.150      0.000       -35.065   -28.230\n",
        "C(State)[T.MS]             -34.3088      1.727    -19.863      0.000       -37.694   -30.923\n",
        "C(State)[T.MT]             -73.3897      1.784    -41.132      0.000       -76.887   -69.893\n",
        "C(State)[T.NC]             -30.5965      1.615    -18.946      0.000       -33.762   -27.431\n",
        "C(State)[T.ND]             -45.3742      1.784    -25.429      0.000       -48.872   -41.877\n",
        "C(State)[T.NE]             -33.6294      1.702    -19.762      0.000       -36.965   -30.294\n",
        "C(State)[T.NH]             -35.2685      2.242    -15.730      0.000       -39.663   -30.874\n",
        "C(State)[T.NJ]              -3.2433      1.789     -1.813      0.070        -6.750     0.264\n",
        "C(State)[T.OH]             -35.1388      1.616    -21.749      0.000       -38.305   -31.972\n",
        "C(State)[T.OK]             -70.7722      1.761    -40.186      0.000       -74.224   -67.320\n",
        "C(State)[T.PA]             -77.5729      1.647    -47.086      0.000       -80.802   -74.344\n",
        "C(State)[T.SC]             -34.8191      1.685    -20.663      0.000       -38.122   -31.516\n",
        "C(State)[T.SD]             -46.4702      1.733    -26.818      0.000       -49.866   -43.074\n",
        "C(State)[T.TN]             -67.0819      1.724    -38.904      0.000       -70.462   -63.702\n",
        "C(State)[T.TX]             -42.1135      1.643    -25.633      0.000       -45.334   -38.893\n",
        "C(State)[T.UT]             -74.4249      1.840    -40.457      0.000       -78.031   -70.819\n",
        "C(State)[T.VA]             -41.4292      1.641    -25.251      0.000       -44.645   -38.213\n",
        "C(State)[T.WI]             -24.9885      1.496    -16.707      0.000       -27.920   -22.057\n",
        "C(State)[T.WV]             -66.9652      1.962    -34.137      0.000       -70.810   -63.120\n",
        "C(State)[T.WY]              17.9853      1.682     10.696      0.000        14.689    21.281\n",
        "ProjectedIndexRate           0.1872      0.002    118.962      0.000         0.184     0.190\n",
        "==============================================================================\n",
        "Omnibus:                    12134.993   Durbin-Watson:                   0.575\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            29738.633\n",
        "Skew:                           0.738   Prob(JB):                         0.00\n",
        "Kurtosis:                       5.301   Cond. No.                     4.16e+04\n",
        "==============================================================================\n",
        "\n",
        "Warnings:\n",
        "[1] The condition number is large, 4.16e+04. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
        "\"\"\""
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Data wrangling step 2 and 4\n",
      "\"\"\"\n",
      "df = urr14[['Company Legal Name','HIOS Issuer ID','Plan ID (Standard Component ID)','State','Index Rate for Projection Period','Plan Section 4 - Average Rate PMPM']]\n",
      "df.columns = ['Company','Issuer','PlanId','State','Index14','PlanIndex14']\n",
      "for c1,c2 in [('Index Rate for Projection Period','Index15'),('Projected Member Months','Mem15'),('Experience Period Member Months','MemExp15'),('Plan Section 4 - Plan Adjusted Index Rate','PlanIndex15'),('Incurred Claims in Experience Period (PMPM)','ClaimsExp15'),('Premiums (net of MLR Rebate) in Experience Period (PMPM)','PremiumsExp15')]:\n",
      "    df[c2] = sub[c1]\n",
      "df['IndexChg'] = df.Index15/df.Index14\n",
      "df['PlanIndexChg'] = df.PlanIndex15/df.PlanIndex14\n",
      "df['MLR'] = df.ClaimsExp15/df.PremiumsExp15\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:7: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "-c:8: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:9: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "-c:10: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Company</th>\n",
        "      <th>Issuer</th>\n",
        "      <th>PlanId</th>\n",
        "      <th>State</th>\n",
        "      <th>Index14</th>\n",
        "      <th>PlanIndex14</th>\n",
        "      <th>Index15</th>\n",
        "      <th>Mem15</th>\n",
        "      <th>MemExp15</th>\n",
        "      <th>PlanIndex15</th>\n",
        "      <th>ClaimsExp15</th>\n",
        "      <th>PremiumsExp15</th>\n",
        "      <th>IndexChg</th>\n",
        "      <th>PlanIndexChg</th>\n",
        "      <th>MLR</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Freelancers CO-OP of New Jersey</td>\n",
        "      <td>10191</td>\n",
        "      <td>10191NJ0030001</td>\n",
        "      <td>NJ</td>\n",
        "      <td>540.757</td>\n",
        "      <td>380.3042</td>\n",
        "      <td>486.4124</td>\n",
        "      <td>139124</td>\n",
        "      <td>1</td>\n",
        "      <td>373.18</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>0.899503</td>\n",
        "      <td>0.981267</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Freelancers CO-OP of New Jersey</td>\n",
        "      <td>10191</td>\n",
        "      <td>10191NJ0030002</td>\n",
        "      <td>NJ</td>\n",
        "      <td>540.757</td>\n",
        "      <td>424.5480</td>\n",
        "      <td>486.4124</td>\n",
        "      <td>139124</td>\n",
        "      <td>1</td>\n",
        "      <td>382.11</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>0.899503</td>\n",
        "      <td>0.900040</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Freelancers CO-OP of New Jersey</td>\n",
        "      <td>10191</td>\n",
        "      <td>10191NJ0050001</td>\n",
        "      <td>NJ</td>\n",
        "      <td>540.757</td>\n",
        "      <td>433.9558</td>\n",
        "      <td>486.4124</td>\n",
        "      <td>139124</td>\n",
        "      <td>1</td>\n",
        "      <td>389.72</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>0.899503</td>\n",
        "      <td>0.898064</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Freelancers CO-OP of New Jersey</td>\n",
        "      <td>10191</td>\n",
        "      <td>10191NJ0050002</td>\n",
        "      <td>NJ</td>\n",
        "      <td>540.757</td>\n",
        "      <td>479.9313</td>\n",
        "      <td>486.4124</td>\n",
        "      <td>139124</td>\n",
        "      <td>1</td>\n",
        "      <td>524.94</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>0.899503</td>\n",
        "      <td>1.093782</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Freelancers CO-OP of New Jersey</td>\n",
        "      <td>10191</td>\n",
        "      <td>10191NJ0050003</td>\n",
        "      <td>NJ</td>\n",
        "      <td>540.757</td>\n",
        "      <td>529.4393</td>\n",
        "      <td>486.4124</td>\n",
        "      <td>139124</td>\n",
        "      <td>1</td>\n",
        "      <td>642.20</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>0.899503</td>\n",
        "      <td>1.212981</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                           Company  Issuer          PlanId State  Index14  \\\n",
        "0  Freelancers CO-OP of New Jersey   10191  10191NJ0030001    NJ  540.757   \n",
        "1  Freelancers CO-OP of New Jersey   10191  10191NJ0030002    NJ  540.757   \n",
        "2  Freelancers CO-OP of New Jersey   10191  10191NJ0050001    NJ  540.757   \n",
        "3  Freelancers CO-OP of New Jersey   10191  10191NJ0050002    NJ  540.757   \n",
        "4  Freelancers CO-OP of New Jersey   10191  10191NJ0050003    NJ  540.757   \n",
        "\n",
        "   PlanIndex14   Index15   Mem15  MemExp15  PlanIndex15  ClaimsExp15  \\\n",
        "0     380.3042  486.4124  139124         1       373.18            1   \n",
        "1     424.5480  486.4124  139124         1       382.11            1   \n",
        "2     433.9558  486.4124  139124         1       389.72            1   \n",
        "3     479.9313  486.4124  139124         1       524.94            1   \n",
        "4     529.4393  486.4124  139124         1       642.20            1   \n",
        "\n",
        "   PremiumsExp15  IndexChg  PlanIndexChg  MLR  \n",
        "0              1  0.899503      0.981267    1  \n",
        "1              1  0.899503      0.900040    1  \n",
        "2              1  0.899503      0.898064    1  \n",
        "3              1  0.899503      1.093782    1  \n",
        "4              1  0.899503      1.212981    1  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Market Size and Change in Projected Index Rate"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Projected Member Months is used as a proxy for market size<br>\n",
      "36% are new entrants with no experience, making Experience Period Member Months a spare feature<br>\n",
      "Cite Aetna and other carriers that chose to be off exchange in 2014, but joint 2015"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for (c,i),g in sub.groupby(['State','HIOS Issuer ID']):\n",
      "    assert len(set(g['Projected Member Months']))==1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Experience Member Months is inadequate. Rampant missing data such as Bridgespan in Oregon."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# weird missing data about those with experience period\n",
      "\n",
      "counter = 0\n",
      "total = 0\n",
      "for (c,i),g in sub.groupby(['State','HIOS Issuer ID']):\n",
      "    if list(set(g['Experience Period Member Months']))[0]>1:\n",
      "        counter+=1\n",
      "    total+=1\n",
      "\n",
      "# percentage of new entrants\n",
      "(1-counter/float(total))*100,total, counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(34.75177304964539, 141, 92)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Projected Member Months as a proxy<br>\n",
      "Market Share calculation as well as Validation from 2013 Kaiser Foundation database"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp = urr.copy()\n",
      "tmp = tmp[tmp['Plan ID (Standard Component ID)'].map(lambda p:p in ffmPlanIds)]\n",
      "\n",
      "res = ''\n",
      "marketLookup = {}\n",
      "compLookup = {}\n",
      "rankLookup = {}\n",
      "for s,g in tmp.groupby('State'):\n",
      "    if len(set(g['HIOS Issuer ID']))>=1:\n",
      "        marketLookup[s]={}\n",
      "        compLookup[s]={}\n",
      "        rankLookup[s]={}\n",
      "        line = s+','\n",
      "        g = g.drop_duplicates(subset=['HIOS Issuer ID']).\\\n",
      "            sort('Projected Member Months',ascending=False)\n",
      "        g['MarketSize'] = g['Projected Member Months']/sum(g['Projected Member Months'])*100\n",
      "        rank = 1\n",
      "        for _,r in g.sort('MarketSize',ascending=False).iterrows():\n",
      "            line += '\"'+r['Company Legal Name']+'\",'+str(r['MarketSize'])+','\n",
      "            marketLookup[s][r['HIOS Issuer ID']]=r['MarketSize']\n",
      "            compLookup[s]=len(set(g['HIOS Issuer ID']))\n",
      "            rankLookup[s][r['HIOS Issuer ID']] = rank\n",
      "            rank+=1\n",
      "        res+=line+'\\n'\n",
      "        \n",
      "# open('MemMthMarket.csv','w').write(res)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Diagrams about Market Share"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# diagram on number of issuers in each state\n",
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()\n",
      "ax.get_yaxis().tick_left()\n",
      "# plt.xticks(range(0,101,10),fontsize=14)  \n",
      "plt.yticks(fontsize=14)  \n",
      "plt.xlabel(\"State\", fontsize=16)\n",
      "plt.ylabel(\"Number of Issuers\", fontsize=16)\n",
      "tmp = sorted(marketLookup.items(),key=lambda x:len(x[1]), reverse=True)\n",
      "x = [t[0] for t in tmp]\n",
      "y = [len(t[1]) for t in tmp]\n",
      "plt.bar(range(len(x)),y,color=\"#3F5D7D\")\n",
      "plt.xticks(map(lambda x:x+.4,range(len(x))), x, rotation=45)\n",
      "# plt.hist(reduce(lambda x,y:x+y,[v.values() for v in marketLookup.values()]),bins = 20,color=\"#3F5D7D\")\n",
      "# plt.title(\"Number of Issuers in each State\", fontsize=16)\n",
      "plt.savefig(\"figures/fig1.png\", bbox_inches=\"tight\");\n",
      "\n",
      "# cache data in csv form\n",
      "pd.DataFrame(zip(x,y), columns=['State','Number of Issuers']).set_index('State').to_csv('figures/fig 1 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MY+3yd+ANuqmNOiL2JD9sdyrTWL38XeV2zKnEHBGbAWcCz2giIYuITYHjImLh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\nkhr0/wGo/30JdDA01wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a93b250>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 2"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.xticks(range(0,101,10),fontsize=14)  \n",
      "plt.yticks(fontsize=14)  \n",
      "plt.xlabel(\"Market Share Within State\", fontsize=16)\n",
      "plt.ylabel(\"Number of Issuers\", fontsize=16)\n",
      "y,x,_ = plt.hist(reduce(lambda x,y:x+y,[v.values() for v in marketLookup.values()]),bins = 20,color=\"#3F5D7D\")\n",
      "plt.title(\"Number of Issuers against Market Share Within State\", fontsize=16)\n",
      "plt.savefig(\"figures/fig2.png\", bbox_inches=\"tight\");\n",
      "\n",
      "# cache data in csv form\n",
      "pd.DataFrame(zip(x,y), columns=['Market Share Within State','Number of Issuers']).set_index('Market Share Within State').to_csv('figures/fig 2 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3YuDGISxHkiRJQzDoWbTH8dCzaNcAnt48BjkJQ5IkSTNg0DF4O/DQBG8xcBVw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\nP+JeQ00KNuia94Nm2l/2ed0SaqLzeOA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       "text": [
        "<matplotlib.figure.Figure at 0x10a8071d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Apply market size mapping to df"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Calculating Market Rank as in data wrangling step 2\n",
      "\"\"\"\n",
      "df['MarketSize'] = df.apply(lambda r:marketLookup[r.State][r.Issuer],axis=1)\n",
      "df['MarketRank'] = df.apply(lambda r:rankLookup[r.State][r.Issuer],axis=1)\n",
      "df['StateIssuer'] = df.State+df.Issuer.map(str)\n",
      "\n",
      "tmp = None\n",
      "for si,g in df.groupby('StateIssuer'):\n",
      "    g['PlanIndexChgAvg'] = g.PlanIndexChg.mean()\n",
      "    g['PlanIndexIncreaseProportion'] = len(g[g.PlanIndexChg>1])/float(len(g))\n",
      "    if tmp is None:\n",
      "        tmp = g\n",
      "    else:\n",
      "        tmp = tmp.append(g)\n",
      "df = tmp\n",
      "df = df.drop_duplicates(subset=['StateIssuer'])\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:4: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "-c:5: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:6: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "-c:11: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Company</th>\n",
        "      <th>Issuer</th>\n",
        "      <th>PlanId</th>\n",
        "      <th>State</th>\n",
        "      <th>Index14</th>\n",
        "      <th>PlanIndex14</th>\n",
        "      <th>Index15</th>\n",
        "      <th>Mem15</th>\n",
        "      <th>MemExp15</th>\n",
        "      <th>PlanIndex15</th>\n",
        "      <th>ClaimsExp15</th>\n",
        "      <th>PremiumsExp15</th>\n",
        "      <th>IndexChg</th>\n",
        "      <th>PlanIndexChg</th>\n",
        "      <th>MLR</th>\n",
        "      <th>MarketSize</th>\n",
        "      <th>MarketRank</th>\n",
        "      <th>StateIssuer</th>\n",
        "      <th>PlanIndexChgAvg</th>\n",
        "      <th>PlanIndexIncreaseProportion</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>651</th>\n",
        "      <td>Premera Blue Shield Blue Cross</td>\n",
        "      <td>38344</td>\n",
        "      <td>38344AK0570001</td>\n",
        "      <td>AK</td>\n",
        "      <td>836.3000</td>\n",
        "      <td>487.0324</td>\n",
        "      <td>1468.7300</td>\n",
        "      <td>31260</td>\n",
        "      <td>62545</td>\n",
        "      <td>540.870</td>\n",
        "      <td>199.309402</td>\n",
        "      <td>299.1675</td>\n",
        "      <td>1.756224</td>\n",
        "      <td>1.110542</td>\n",
        "      <td>0.666213</td>\n",
        "      <td>24.447468</td>\n",
        "      <td>2</td>\n",
        "      <td>AK38344</td>\n",
        "      <td>1.366418</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>763</th>\n",
        "      <td>Humana Insurance Company</td>\n",
        "      <td>44580</td>\n",
        "      <td>44580AL0360001</td>\n",
        "      <td>AL</td>\n",
        "      <td>337.8200</td>\n",
        "      <td>159.2000</td>\n",
        "      <td>428.3300</td>\n",
        "      <td>80704</td>\n",
        "      <td>26710</td>\n",
        "      <td>210.250</td>\n",
        "      <td>90.327566</td>\n",
        "      <td>146.3580</td>\n",
        "      <td>1.267924</td>\n",
        "      <td>1.320666</td>\n",
        "      <td>0.617169</td>\n",
        "      <td>2.887934</td>\n",
        "      <td>2</td>\n",
        "      <td>AL44580</td>\n",
        "      <td>1.333553</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>816</th>\n",
        "      <td>Blue Cross and Blue Shield of Alabama</td>\n",
        "      <td>46944</td>\n",
        "      <td>46944AL0300001</td>\n",
        "      <td>AL</td>\n",
        "      <td>375.1600</td>\n",
        "      <td>444.8323</td>\n",
        "      <td>441.7284</td>\n",
        "      <td>2685000</td>\n",
        "      <td>1428120</td>\n",
        "      <td>508.520</td>\n",
        "      <td>184.183460</td>\n",
        "      <td>188.4623</td>\n",
        "      <td>1.177440</td>\n",
        "      <td>1.143172</td>\n",
        "      <td>0.977296</td>\n",
        "      <td>96.080764</td>\n",
        "      <td>1</td>\n",
        "      <td>AL46944</td>\n",
        "      <td>1.143177</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1040</th>\n",
        "      <td>Celtic Insurance Company</td>\n",
        "      <td>62141</td>\n",
        "      <td>62141AR0080001</td>\n",
        "      <td>AR</td>\n",
        "      <td>503.0122</td>\n",
        "      <td>497.9885</td>\n",
        "      <td>427.9816</td>\n",
        "      <td>594087</td>\n",
        "      <td>2856</td>\n",
        "      <td>398.846</td>\n",
        "      <td>160.149860</td>\n",
        "      <td>271.8725</td>\n",
        "      <td>0.850837</td>\n",
        "      <td>0.800914</td>\n",
        "      <td>0.589062</td>\n",
        "      <td>18.279201</td>\n",
        "      <td>2</td>\n",
        "      <td>AR62141</td>\n",
        "      <td>0.815606</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1220</th>\n",
        "      <td>QCA Health Plan, Inc.</td>\n",
        "      <td>70525</td>\n",
        "      <td>70525AR0070003</td>\n",
        "      <td>AR</td>\n",
        "      <td>337.3635</td>\n",
        "      <td>311.4154</td>\n",
        "      <td>453.8100</td>\n",
        "      <td>147984</td>\n",
        "      <td>85518</td>\n",
        "      <td>428.310</td>\n",
        "      <td>119.963250</td>\n",
        "      <td>153.1834</td>\n",
        "      <td>1.345166</td>\n",
        "      <td>1.375366</td>\n",
        "      <td>0.783135</td>\n",
        "      <td>4.553254</td>\n",
        "      <td>3</td>\n",
        "      <td>AR70525</td>\n",
        "      <td>1.372366</td>\n",
        "      <td>0.964286</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                                    Company  Issuer          PlanId State  \\\n",
        "651          Premera Blue Shield Blue Cross   38344  38344AK0570001    AK   \n",
        "763                Humana Insurance Company   44580  44580AL0360001    AL   \n",
        "816   Blue Cross and Blue Shield of Alabama   46944  46944AL0300001    AL   \n",
        "1040               Celtic Insurance Company   62141  62141AR0080001    AR   \n",
        "1220                  QCA Health Plan, Inc.   70525  70525AR0070003    AR   \n",
        "\n",
        "       Index14  PlanIndex14    Index15    Mem15  MemExp15  PlanIndex15  \\\n",
        "651   836.3000     487.0324  1468.7300    31260     62545      540.870   \n",
        "763   337.8200     159.2000   428.3300    80704     26710      210.250   \n",
        "816   375.1600     444.8323   441.7284  2685000   1428120      508.520   \n",
        "1040  503.0122     497.9885   427.9816   594087      2856      398.846   \n",
        "1220  337.3635     311.4154   453.8100   147984     85518      428.310   \n",
        "\n",
        "      ClaimsExp15  PremiumsExp15  IndexChg  PlanIndexChg       MLR  \\\n",
        "651    199.309402       299.1675  1.756224      1.110542  0.666213   \n",
        "763     90.327566       146.3580  1.267924      1.320666  0.617169   \n",
        "816    184.183460       188.4623  1.177440      1.143172  0.977296   \n",
        "1040   160.149860       271.8725  0.850837      0.800914  0.589062   \n",
        "1220   119.963250       153.1834  1.345166      1.375366  0.783135   \n",
        "\n",
        "      MarketSize  MarketRank StateIssuer  PlanIndexChgAvg  \\\n",
        "651    24.447468           2     AK38344         1.366418   \n",
        "763     2.887934           2     AL44580         1.333553   \n",
        "816    96.080764           1     AL46944         1.143177   \n",
        "1040   18.279201           2     AR62141         0.815606   \n",
        "1220    4.553254           3     AR70525         1.372366   \n",
        "\n",
        "      PlanIndexIncreaseProportion  \n",
        "651                      1.000000  \n",
        "763                      1.000000  \n",
        "816                      1.000000  \n",
        "1040                     0.000000  \n",
        "1220                     0.964286  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Index Rate Change Diagram\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 3"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.xticks(fontsize=14)  \n",
      "plt.yticks(fontsize=14)\n",
      "plt.xlabel(\"Premium Changes from '14 to '15 (%)\", fontsize=16)\n",
      "plt.ylabel(\"Frequency\", fontsize=16)\n",
      "x,y,_ = plt.hist(df.IndexChg.map(lambda x:(x-1)*100),bins=20,color=\"#3F5D7D\")\n",
      "\n",
      "x0 = df.IndexChg.map(lambda x:(x-1)*100).mean()\n",
      "plt.annotate('Mean: {:0.1f}'.format(x0), xy=(x0, 1), xytext=(-15, 15),\n",
      "        xycoords=('data', 'axes fraction'), textcoords='offset points',\n",
      "        horizontalalignment='left', verticalalignment='center',\n",
      "        arrowprops=dict(arrowstyle='-|>', fc='black', shrinkA=0, shrinkB=0,\n",
      "                        connectionstyle='angle,angleA=0,angleB=90,rad=10'),fontsize=14\n",
      "        )\n",
      "\n",
      "plt.axvline(x0, color='r', linestyle='dashed', linewidth=2)\n",
      "plt.savefig(\"figures/fig3.png\", bbox_inches=\"tight\");\n",
      "\n",
      "# cache data in csv form\n",
      "pd.DataFrame(zip(x,y), columns=[\"Frequency\",\"Premium Changes from '14 to '15 (%)\"])\\\n",
      "    .set_index(\"Frequency\").to_csv('figures/fig 3 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Auydw9jz7zgV2HktpJEmStKhRA9w1wE7z7NsRuH48xZHUZ6ccc+ja9VAlSctn1AD3ZeAl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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a808890>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.xticks(range(19),fontsize=14) \n",
      "plt.yticks(fontsize=14)\n",
      "plt.xlabel(\"Market share ranking within state\", fontsize=16)\n",
      "plt.ylabel(\"Issuer premium changes from '14 to '15 (%)\", fontsize=16)\n",
      "plt.scatter(df.MarketRank,df.IndexChg.map(lambda x:(x-1)*100),color=\"#3F5D7D\")\n",
      "\n",
      "plt.axhline(0, color='r', linestyle='dashed', linewidth=2)\n",
      "plt.savefig(\"figures/fig4.png\", bbox_inches=\"tight\");\n",
      "\n",
      "\n",
      "threshold = df.State.map(lambda s:100./compLookup[s])\n",
      "threshold=50\n",
      "lower = df[df.MarketRank>=2].IndexChg\n",
      "higher = df[df.MarketRank<=1].IndexChg\n",
      "print np.mean(lower),np.mean(higher)\n",
      "print stats.ttest_ind(lower, higher)\n",
      "\n",
      "# cache data in csv form\n",
      "pd.DataFrame(zip(df.MarketRank,df.IndexChg.map(lambda x:(x-1)*100)), columns=[\"Market share ranking within state\",\"Issuer premium changes from '14 to '15 (%)\"])\\\n",
      "    .set_index(\"Market share ranking within state\").to_csv('figures/fig 4 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.16293245582 1.23150666994\n",
        "(-1.2521202005457188, 0.21262883687103476)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RpPGplLxFxOW0FCoMsYqiR+x/gf/IzDs7FNtQpwO/BK4q7+9Q/jv0+e4CduxS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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10efe3710>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Results"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Largest issuer of each state raised premiums on average 10.3% higher than other same-state issuers. Statistically significant with a one-tailed paired t-test."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diffs = []\n",
      "g1 = []\n",
      "g2 = []\n",
      "for s,g in df.copy().groupby('State'):\n",
      "    if 1 in g.MarketRank.tolist() and len(g.MarketRank)>1:\n",
      "        t = g[g.MarketRank>=2]\n",
      "        if t.empty:continue\n",
      "        diffs.append(g[g.MarketRank==1].IndexChg.irow(0)-sum(t.MarketSize/sum(t.MarketSize)*t.IndexChg))\n",
      "        g1.append(g[g.MarketRank==1].IndexChg.irow(0))\n",
      "        g2.append(sum(t.MarketSize/sum(t.MarketSize)*t.IndexChg))\n",
      "\n",
      "# print statistical test results\n",
      "print 'Mean: %s'%np.mean(diffs)\n",
      "testStat,pVal = stats.ttest_1samp(diffs,0) \n",
      "print 'test-statistics: %s, one-tail p-value: %s'%(testStat,pVal/2.)# this p value is for 2 tail so we divide by two to get one tail\n",
      "\n",
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.xticks(fontsize=14)  \n",
      "plt.yticks(fontsize=14)\n",
      "xlabel = \"Largest issuer's premium change minus market share weighted \\naverage premium change of other issuers in the same state (%)\"\n",
      "plt.xlabel(xlabel, fontsize=16)\n",
      "plt.ylabel(\"Frequency\", fontsize=16)\n",
      "\n",
      "diffs = map(lambda x:x*100,diffs)\n",
      "\n",
      "x0 = np.mean(diffs)\n",
      "plt.annotate('Mean: {:0.1f}'.format(x0), xy=(x0, 1), xytext=(-15, 15),\n",
      "        xycoords=('data', 'axes fraction'), textcoords='offset points',\n",
      "        horizontalalignment='left', verticalalignment='center',\n",
      "        arrowprops=dict(arrowstyle='-|>', fc='black', shrinkA=0, shrinkB=0,\n",
      "                        connectionstyle='angle,angleA=0,angleB=90,rad=10'),fontsize=14\n",
      "        )\n",
      "\n",
      "plt.axvline(x0, color='r', linestyle='dashed', linewidth=2)\n",
      "y,x,_ = plt.hist(diffs,bins=10,color=\"#3F5D7D\")\n",
      "plt.savefig(\"figures/fig5.png\", bbox_inches=\"tight\");\n",
      "\n",
      "# cache data in csv form\n",
      "pd.DataFrame(zip(x,y), columns=[xlabel,\"Frequency\"])\\\n",
      "    .set_index(xlabel).to_csv('figures/fig 5 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean: 0.102592432056\n",
        "test-statistics: 1.97046015076, one-tail p-value: 0.0304696303992\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kSZJ6xgBOkiSpZ2yFKkkjcCxUSZPEEjhJkqSeMYCTJEnqGQM4SZKknjGAkyRJ\n6hkDOEmSpJ6xFaokjcCxUCVNEkvgJEmSesYATpIkqWcM4CRJknrGAE6SJKlnDOAkSZJ6ZmytUJO8\nAHgFsAL4JfDSqvrWLGm3A06eYdYjq+rwpcqjJE1zLFRJk2QsJXBJ/g54N/AmYAfgO8BhSW47z6KP\noAV806+jljKfkiRJk2hcj1D3AT5eVR+tqhOr6p+Ac4Dnz7PcBVV13sDrT0ufVUmSpMmy7AFckhsC\n9wGGH30eDjxwnsU/n2RVkm8lefKSZFCSJGnCjaME7hbAJsCqoenn0R6LzuQS4OXAU4BHAV8HPp1k\nj6XKpCRJ0qTqxVBaVXU+cMDApB8luTnwSuCT48mVJEnSeIwjgPs9cA2w9dD0rWn14EZ1PLD3TDNW\nrlx57f9TU1NMTU0tKIOSNMyxUCVNkmUP4KrqqiQ/BB4OfG5g1q7AZxewqh2As2eaMRjASZIkbWjG\n9Qh1f+CQJN+ndSHyPFr9tw8AJHkrsGNVPax7vydwFfATYDXwOOAFtEeokiRJG5WxBHBV9ZmuDtu+\nwK2AnwOPrqozuiQrgO0HF+nSbkt7/HoisFdVHbp8uZYkSZoMY2vEUFXvB94/y7y9ht4fDBy8HPmS\nJEmadI6FKkmS1DO96EZEksbNsVAlTRJL4CRJknrGAE6SJKlnDOAkSZJ6xgBOkiSpZwzgJEmSesZW\nqJI0AsdClTRJLIGTJEnqGQM4SZKknjGAkyRJ6hkDOEmSpJ4xgJMkSeoZW6FK0ggcC1XSJLEETpIk\nqWcM4CRJknrGAE6SJKlnDOAkSZJ6xgBOkiSpZ2yFKkkjcCxUSZPEEjhJkqSeMYCTJEnqGQM4SZKk\nnjGAkyRJ6hkDOEmSpJ6xFaokjcCxUCVNEkvgJEmSesYATpIkqWcM4CRJknrGAE6SJKlnDOAkSZJ6\nxlaokjQCx0KVNEksgZMkSeoZAzhJkqSeMYCTJEnqGQM4SZKknjGAkyRJ6hlboUrSCBwLVdIkGUsJ\nXJIXJDklyeVJfpDkQfOkv2eSY5JcluTMJPstV14lSZImzbIHcEn+Dng38CZgB+A7wGFJbjtL+i2A\nI4BzgPsCLwFekWSf5cmxlssfzj113FnQevD4SePhtddvSabWZblxlMDtA3y8qj5aVSdW1T/RgrPn\nz5J+D2AzYM+qOqGqPge8vVuPNiB/WHXauLOg9eDxk8bDa6/3ptZloWUN4JLcELgPcPjQrMOBB86y\n2E7AsVWk+a3gAAAgAElEQVR15VD6bZJsu/i5lCRJmmzL3YjhFsAmwKqh6ecBK2ZZZgVw+tC0VQPz\n1vmnx9VXX8WVl12yrotPpD9ddcW4syBJkpZYqmr5PizZBjgT2LmqvjUw/f8BT6uqu8ywzNeAM6rq\nHwam3Q44Fdipqr43lH75NkiSJGk9VdWCG7gvdwnc74FrgK2Hpm9Nqwc3k3NZu3Ru64F5a1iXnSBJ\nktQny1oHrqquAn4IPHxo1q601qgzOQ54cJJNh9KfVVXW3JQkSRudcbRC3R94VpJnJ7lrkvfQStg+\nAJDkrUmOHEh/KHAZcFCSuyfZDXhVtx5JkqSNzrKPxFBVn0lyc2Bf4FbAz4FHV9UZXZIVwPYD6S9O\nsivwPuAHwAXAO6vqgOXNuSRJ0mRY1kYMkiRJWn8b1GD2aQ5LsjrJk4fmbZnkkCR/6F4HJ7npuPKq\npjsuByb5VTdU2ulJ/j3Jn8+QzuM3oRY6PJ6WX5LXJDk+yUVJzkvypSR3nyHdyiRnddfjUUnuNo78\nanbdsVyd5MCh6R67CZXkVkk+0V17lyf5ZZKdh9Is6PhtUAEc8HJaK1eA4aLFQ2lDdz0CeCStQ+FD\nli9rmsU23esVwD2ApwM7A58aSufxm1ALHR5PY7ML8F5a5+gPBa4Gjkyy5XSCJK+ijXLzImBHWh+d\nRyS5yfJnVzNJ8gDgOcDPGPie89hNriQ3A75NO16PBu5CO07nDaRZ+PGrqg3i1W3w6cBWwGpgt4F5\nd+2m7TQw7a+7aXcad959rXUsH0ULxG/i8Zv8F/A94IND034DvGXcefM153G7MS2Ie0z3PrTunF4z\nkGYz4GLguePOr68CuCnwf7Rg/Cjg3zx2k/8C3kIbUWq2+et0/DaIErgkm9NKaJ5TVb+bIclOwB+r\n6riBad8BLu3mabLcFLiS1voYPH4Tax2Hx9Nk2IL2FObC7v3taX1sXnssq+oK4Jt4LCfFh4DPVtUx\ntC/9aR67yfZE4PtJPp1kVZIfJ3nhwPx1On4bRABH64Lkq1X1tVnmrwDWCOyqhbhzDeGlMeiKmt8I\nfKiqVneTPX6Ta12Gx9NkeA/wY1pfm3Dd8fJYTqAkz6H10LBvN2mwmpDHbrJtD7yAVnr6cNq197aB\nIG6djt+ydyMyqiRvAl47T7KHALcD7gXct1tu+leJIzKM0YjHb6qqvjmwzE2ALwNnAK9cwuxJG7Uk\n+9N+2T+o+zE0H7srGKMkdwbeTDte0/W8w2jfcx678bse8P2q+pfu/U+T3BF4Ia2LtLnMevwmNoAD\nDgAOnifNGcCzgLsBf7wudgPg00m+U1U704bc2mpwZhfo3ZIZhuPSohj1+AHXBm9fpdVre2y1UTum\nefwm17oMj6cxSnIAsDvwkKo6dWDW9LW0NW3Magbee52N10600u5fDnzPbUIbpegfaQ3AwGM3qc4G\nThia9mtaARSs47U3sQFcVZ0PnD9fuiT/ArxjcBKtc+CXA//dTTsOuEmSnQbqUe1Eq8Q72xBeWg+j\nHj+4tg7jYbRfGo+qqsuGknj8JlRVXZVkeni8zw3M2hX47Hhypdl0I988hRa8/WZo9im0L4uH04Y8\nJMlmwIOAf17OfGotXwC+P/A+wMfpGgsBJ+Gxm2TfprU8HXQn4NTu/3W69iY2gBtVVZ1Ni26v1f1C\nOWP612VV/SrJ/wIfTPJc2sn/QeDLVXXS8uZYg7rg7XBgc1pFz827aQDnV9WfPH4Tb3/gkCTfpwXU\nz2NgeDxNhiTvo3XT80TgoiTTdWsuqapLq6qSvBt4bZJf04KCfYFLaI3ENCZVdRFw0eC0JJcBF1bV\nCd17j93kOgD4TpLXAp8B/hJ4MfAaaHW61+X49T6AW4CnAQcC0w0d/pvW34rG66+A+9NK3wZLBIpW\nx3G6jpzHb0LV/MPjaTI8n3ZdfX1o+krgDQBV9a9J/oxWL2dL4LvAw6vq0mXMp0ZTDNSP8thNrqr6\nQZIn0kpL9wNOA/atqvcPpFnw8XMoLUmSpJ7ZULoRkSRJ2mgYwEmSJPWMAZwkSVLPGMBJkiT1jAGc\nJElSzxjASZIk9YwBnCRJUs8YwC1QkmclWZ1k+3HnZTElWZnkIQtIu3qp87RUkhyU5Khx52O5JDm6\nT9s7fX4l8f40JMlUt292HndeJk13Xa9359FJbtqdg3+5SPnaIL8zFiLJqUk+tg7Lbdftu2ePkHbk\n77AF5mF1ktct9noXgzdITft/tJEPRvFh4AFLmJeltkYP5huB59F64e+Tjen4LMQPadfej8edkQm1\nGOfNlrT74aIEcALgCcAb12P5UY7rQr7DluLzl93GNJRWLyS5YVVdNa6PHyVRVZ0FnLXEeVl0A/s2\njLity5CXJVdVv16Oz1lkYz0+k6qqLmHNQc03ekk2raorp98u5qoXcV1LamgfTJyq+ukyfVRvjtli\nsARuCSTZMcl/JTkjyWVJfp3kzUk2G0p3dJJjkzwuyY+TXEFXUpLkPt28y5KcnuQ1SV4//OgyyfW7\neb9OckWSs5K8M8mmQ2nemOS3SS5P8rtu3X/dzZ9e5790xcWrk/y/ObZvrUeoSV6S5Fddfi9Icnw3\n9tv0/Eck+U6SPyS5pMvvfgPzD0pyygyftdbjvyRbJflAkjO7bf5VkucMpZl+bPHgJJ9NciFtbDkY\nKoFLcpMkByY5rVvfqiRHJLnzbPugW+7UJIckeU6S/+v27Q+TTA2lO6g7F3bq9sFlwL+uw7Y8sDuv\nLk5ybpJXd/Mfm+SnSS5N8v0k95lrHw6s73ZD6WY6rqu7c+cV3Xn4xyT/0+X7Vkk+l+Sibt+9cq79\nNbDOrZL8e7dPrujWe3CSGw4l3T7JV7rz5dQk+yXJwHo2TXJAkp93ac5J8qXh4zawvfdP8skuv2cl\nec/gddKl3T7JV7t9uSrtWnruLPvrud1+n76mPpJkyxG2f/q8eWaS36RdM99Mcsckmyf5aJLzu2P8\njiSbDCy71iPUXHcfeViSH3V5/3kGrr8u3UjX2CJcD+uyXQs9loPX9XFz5GmvJFcOnptzHbck2wEn\nd0k/nOvuh8+c4zN27PbP77tt/m2S982QdKsRzr/Xd8fwoi5vX09y/6E00+fAk5J8OMnvgHNH2b45\ntuHAJCcNTfth9zl3GJj25iTnDqXbLcl3u/PuwiSfSXLboTSnJvn40LSHpX3vXZ7kpCTPnu0cBa6f\n5A1Jzu4+40tJbj2wrjm/w5Ls0u3Li9PuYf+b5O5D+dkkyZu6c+/SJEcNp5k0lsAtjdsBPwU+AfwB\nuAeteHd74O8H0hVwJ+A9tMGkTwYuSHIL2oDTZwLPBP4EvAy4PWsX5f4H8FjgbcB3gLvRiqq3A/62\nS/Mq4KXAa4GfADelDSI/fVHvRLsJfhz4YDftzHm2cTAA2gN4J/B64Fjgz4B7T68/re7Hl4DP0AbO\nvqrb7tvPts6haYOftQXwLWBT4HXAKcAjgfen/Qp979DynwQOBd5Pd75X1V5DaQ4AHge8BjgJuAXw\nQOBmc+6Blq8p4D7dslfR9vVhSe5dVb8ZSHtT4FPAO4BXA5evw7YcRDun/h3YHXhLkq2Bh9GO+aW0\nwPCLSe5QVX8ayOeojwBmSvdM4GfAPwIrgHfTzrstgS/SBl/eHXhbkp9X1WGzrbz7IvkObd++qVvv\n1sDjgRvQ9uG0LwAfA97VzX89cEa3H6Dtt81pA0Sf1eXnhcBxSe5aVauGPv4Q2rnwJNrxXQlc2P0l\nLYA8osvH84DfA/8APGV4vyR5G7AP7dp9OXCbbnvukeSBVTVXHdECdqbdD/652453A5+jXXe/oO3P\nXYB9afeF98+4puvWd4duHW8Bzu/y9Nkkd6mq3w6lnWn5wenrcz2s63Yt9FiudV0PS/Ja2nX1nKo6\nuJs253EDzgZ2Az7f5eVL3epOZgZJbgJ8jfbjcE/gEtp9bacZks95/nVuTdtnpwE3Bp4BfDPJX1XV\nL4bWdyDwVWAPYLNRtm+O8/IbwAuT3Laqzuiu0x2Ay4CHAtPn0EOBwWD/ebT70ce67dii+3tMkntV\n1R+7pMP38bsBX+n229/Rjv9+tPvkNTPk7zXAt4G9aPeLd9HuQdOPTGf9DkvyGOC/gS93+yq0+/Sx\nXR6nv+tWdp/zLuBwYEeuO/6Tqap8LeAFPAtYDWw/YvrQbjBPp52YWw7MO7qbdq+hZd4CXA5sMzBt\nM2AVcM3AtAd3edljaPmnddPv1b3/H+C/5snnauANI27TSmD1wPv3Aj+cI/3fduu/yRxpDgJOmWH6\n0cA3Bt7v1+2bOwyl+xDwO+B6Q8fpXSNsz8+Bd67DuXAqcAVw64FpN6F9gR48tG2rgccNLb/Qbdl3\nIM0mwHm0gGfbgemP69LuPMc+nF7f7eY6rgPnxa+n89JNe1c3/bVD+VkFfGyeffYG4Grg3vOdX8Ce\nQ9N/BnxtjuWuB9wIuBh46Qzb+7qh9F8GThx4/9wu3X2H0v2Edp3ernu/XbcN+w6le2C3/BNGOG9+\nD2w+MO3F3bIfGkr7w6FjNzXL8b1y8DwCtury+Jp1uMbW53pYp+1ah2O51nXdbd/ptHvugcAfgUcN\nzB/puHXpVgN7j7DN9+3S3mOONCOdfzMstwntu+PXwLtnOAc+N5R+nc9L4M+7c/wZ3fsnAhcAHwEO\n7abdhHa/ee7A+4uAj8yQjyuBlwxMO4WBewMtkF0FbDYwbQXtfnry0LpWD58rtOB0NbBiYNqM32HA\n/wFHDE3bnHaPPaB7v2V3vvz7ULpXduv9fwu9Hpbj5SPUJZBkiyRvT/Jb2gl5FXAw7cZyp6Hkp1TV\nz4amPQD4blWdPT2hqq6g/WIZfMb/yG7dn097THr9JNenlSJA+zUMrc7MY7ri4Qdl7UdV6+v7wA5J\n/q0rFr/R0Pwf00oRP53kyUluuR6f9Ujar7ZTh7b5cODmtBLIQV8YYZ3HA3ulPYq+bwYe7Yzgu9Xq\nBAJQ7RfnV1j7F/hVtEB6fbbl2pKtqrqGdmM6sapOG0hzYvf3NgvYhvkcUWv+cp/+jK/NkJ/5Pvfh\nwPdrtDoxXxl6/0ta6fa1kuye5Htpj9Kupt2Eb8La19lM6/vF0PoeAJxWVT8YSvd51rzudqUFGIcO\nHbfvd58/SgvR46rVZ5u21j4dmH5b5ndSDZS0VdXvaAH+KMsOW5/rYZ23a4HHcrbr+gbAp2lPOv6m\n1iwNXozjNuw3tKcsH0qyx/CjwyHznX/TjxWPSvJ72j1z+mnFKPtgnbevqi6gPTX6m27SQ2mB/ZFc\nV8q1My2gnC6B24kWCA1/3pm04zvX/nwA8NXue206D+fSStlm8tWh99OlkbcbTjgoyR1pJcLDebyc\ndu+dzuM9aT8YPjO0iv+ca/3jZgC3ND5Oe9z0btrjrfvSHgdAKyoedM4My9+KdvMdNvwY4ZbADWmP\nzq4aeK2iFVffvEv3FtqjhMcD3wR+n+RjSW7OIqj2eOL5wP2B/wXOT6sbtW03/7fAI2jn2yHAOUmO\ny7p1hXBL2iOY6Zvb9OszrLnN02bav8NeTCt235t2s1uVZP8kfzbCssPHBNqxu/XQtN9V95NuwEK3\n5cKh91fNMg26RyqLZLbPGJ7+pxE+9+bM/3h+2gVD768cXH+Sx9FusL+kfWHfj/bY43ez5GOm9Q1e\njwu57qAFrFcNvW5MK82YSzH6Pr2K0Y7l8LbB0P5agHW9HtZ5u9bhWM52XW8BPJr2mP74oXnre9zW\nUlUX0wKcs2mPEk9Lq8e32wzJ5zz/0uqufpVW6rg37X66Iy2wGmUfrO/2HcV1wdpDuvdHAVsnuWs3\n7ayqmq4rN/15R87wefeY5/NWMPO1NtM0mHnfwfzn93QePzpDHh8zkMdbdX+Hr/XZ8jMRrAO3yNIa\nKjyeVlx+4MD0e8+yyEx1Us6mPecfNjztfFoJ34NmWfc5AFV1Na1u1L92pV+PA/an/eJ46izLLkhV\nfYj2K/SmtGDtXbRfwg/o5h8NHJ3kBl1+3wB8Jcm23a+/K2jB6LCb027i035Pq7D7klmy8puh9/PW\n/aqqS2n1A1/b/YJ+Cq1O4VW0+mpzWTHDtK0ZLUhZ6LYslulfvcP7e1EC+nn8jsUrHXwqreRp7+kJ\n3fm1rttxDnDXGabPdN1BK/EYDkwG50+aka6x9bwe1tVCj+Vs1/X5tOoqX6GVuuzRlQ5Pz4NFPm5d\nafLfpvVbuCOtHtVnunqwv1zAqp5M28e7DeSZJH8+S36H98H6bt/RwMuS7EQr/f9GVa1K8itaidwa\n9d8G1rcnLfAedskM06adw2jfcetrOo+vpgWaw6Z/YEwHw1sDv1rC/CwqA7jFtymt7sLVQ9OftYB1\nfBf45yS3nn481/36fQxrXrSH0Z7R36yqvjHKiqvqPOCjXcXOwRY2V9EaH6yXqrqIdvN6AK1O0fD8\nPwFHJXkHrQL87Wm/rk6j/dK7RVX9HiCt9dOdWTOA+19aCcEZ3WOiRVVVZwD7J3k6a+6f2TwgyW2q\nqwibZHPacfry8KpnWHZJt2UO049c70n7tU73WOHhLH1/R4cD+3aVh4erDizUjVi7wvMzWPcnC8cB\nz0qyY1UdD5AktC/Wwf1yOK1ezLZV9fV1/KyFWKxjMuo1dt0HL/x6WFeLdiyr6ptJHkUrzfpUkr/v\nAqJRj9t06c6C7oddNYPvpbV+fDxwF2YObGZzoy5/10ryUNqj5t/OuMSa1ve8PIZ2DN5Ae2Iwnfdv\n0K6Be9PqO0/7Ni1Iu2NVHbLAz/ou8Ogkf1ZVlwMkuRXw16x7F1VrfYdV1a+TnEqro/ivcyz7M9qT\nrL+jBbLTFqWAY6kYwK27RyUZLm79Q1UdmeS7wMuTnEP7BbA3sM0s65mp35r9aY8kv5bk9bQTcx/a\nL+hrb+ZVdUySTwH/lWR/2iOD1bSKn48CXllV/5fkv2kVsX9M+2X2l7RSsg8MfOYJwGOTfI1Wp+Os\nqhrl8SNJPkQr9v8urcj5TrRfwV/r5j+P1uDiq7SSqVvQfqWexXV1GT5Du3H8R5IDujSvpn2xDO6j\nA2gX2bFdut/QHg/cBXhQVa3RdcKI+T+O1krpF7S6IrsA96I9Cp/PKuDwJCu5rhXqn7F2p5UzHefF\n2JZR+z0aTPd92hfCO7pSg6uAF9BKZ9a3H6X5lj+A1sjmyCRvou3zW9C+8J5X17VaG8VhwBO6c/8r\ntKoKL6Kdv+uyHQfRjt/nk/wL17VCvVm3vtUAVXVykrcD703r5uKbtGvztrQqEx/pSpxns9C8jZJ+\npjTD00a6xtbjelifc2exjmUAqupbSR7ZrffTSZ66gOO2inbf/vskP6e1xDy5e1Kw5oclj6X9UP0C\nrRHHjYF/ot0PZ+3eZBaH0UrjD0pyEO0+ui/tPjnvPljf87KqLk7yI1o9uMG6YEfRqgAVLZibTn9J\nklcA70uyFe0H6UW06iO7AEdV1ae65MP5fxOtcdvXkryT9ih0P9oTiXUd5We277AXAv+dVvf7s7Tr\nemta447TquqAqvpDd038S5JLaPXId6R9d0+ucbei6NuLVly8epbXz7o023JdXYZVwL/R6mVcw5qt\nx44CvjnL5/wlrUuOy2ldJ/wLrU7dBUPpQrth/KRL+4fu/7cBW3Rp9qHdTH5Puxn9itatySYD63kg\n8INuHXO2uqHVpxtsDfvMbltW0bUioj1CvUk3/wG00rbTu/ln0x6v3nFovU+gtYC7jBZsPqxb73AL\npJvRgtyTab+WV9F+Pf7TQJpndft73tbC3b76Ubfv/kirc/KiEZY7hdY45dm0kqwraK3rpobSfRw4\nfZZ1rPO2zHT+MEMLuln24d266ZfQvnheOnxcu3RrtexaSH5m2eataHWszu62+fRuH91w8PxioOXr\nwH4cbKEWWqB8Fu3X81G0rg+GW7zNlt+Ztnd7WgBxWXcsDuC6lmibD6V9Ou26+mO3H0+gXevbzLP9\npzDQSrmbNtXl8aFznTsD6ea9jwzvh1GvMdbzeljH7VqvYznbdUa79/yB1hDlBqMet24//ZL24+Ya\n4JmzbPOdaHX3TqbdO8+jNVbacR3Pvxd167oM+B7XPbYcbom81j5d3/Ny4NhfQ9fStJu2ZTft5FmW\neRQtsLuoO3a/obVevcs85+LDunPwCtr98zndcfrhQJrtmKFFMDNfB7N+h3XnwZdpT3su7/JzKHD/\ngTTX687Bc7r9/w1alYqJbYWaLuOacF1LsB8B51XVruPOjyCtw8ljq2rWTj7Vf0n+B7hzVd1x3HmR\nNlRpfer9H/DlqnrOfOnlI9SJleSNtJP5NFpF3n+gtex59DjzpTVsVMO2bAyS7EMruTiJ1kXCU2jX\n3PPGmS9pQ5PkQFpr4bNpVYxeQuvI9z3jzFefGMBNrtW0OgHb0Ooe/BR4YlUN96ek8bH4esNzBe1x\n8u1ojZF+DTy7qkapDylpdJvSHtluTXtU/T3gYbX2iBOahY9QJUmSesaOfCVJknrGAE4bjSRTSVav\n4wgQY9Hld7hLko1OktcmOT3Jn7quDtZ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       "text": [
        "<matplotlib.figure.Figure at 0x109b10350>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 6"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# diagram on number of issuers in each state\n",
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.yticks(fontsize=14)  \n",
      "plt.xlabel(\"\", fontsize=16)\n",
      "plt.ylabel(\"Average premium changes from '14 to '15 (%)\", fontsize=16)\n",
      "x = ['Largest issuers in states','Other issuers']\n",
      "y = [(np.mean(g1)-1)*100,(np.mean(g2)-1)*100]\n",
      "plt.bar(range(len(x)),y,color=\"#3F5D7D\")\n",
      "plt.xticks(map(lambda x:x+.4,range(len(x))), x, fontsize=16)\n",
      "# plt.hist(reduce(lambda x,y:x+y,[v.values() for v in marketLookup.values()]),bins = 20,color=\"#3F5D7D\")\n",
      "# plt.title(\"Number of Issuers in each State\", fontsize=16)\n",
      "plt.savefig(\"figures/fig6.png\", bbox_inches=\"tight\");\n",
      "print np.mean(g1),np.mean(g2)\n",
      "\n",
      "# data\n",
      "pd.DataFrame(zip(x,y),columns=['Issuer Type',\"Average premium changes from '14 to '15 (%)\"]).set_index('Issuer Type').to_csv('figures/fig 6 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.23937948747 1.13678705541\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WT1G2BHAZJUmSpFnqmpD9N/Dq/o1JAvwrcOQgg5IkSRonXe+y/A2wS5JfAF+h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\nNta+bwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a9b2f50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Not only is the dominant player able to raise rates, it raises rates on a larger proportion of its plans."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 7"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diffs = []\n",
      "for s,g in df.copy().groupby('State'):\n",
      "    if 1 in g.MarketRank.tolist() and len(g.MarketRank)>1:\n",
      "        t = g[g.MarketRank>=2]\n",
      "        if t.empty:continue\n",
      "        diffs.append(g[g.MarketRank==1].PlanIndexIncreaseProportion.irow(0)-sum(t.MarketSize/sum(t.MarketSize)*t.PlanIndexIncreaseProportion))\n",
      "\n",
      "# print statistical test results\n",
      "print 'Mean: %s'%np.mean(diffs)\n",
      "testStat,pVal = stats.ttest_1samp(diffs,0) \n",
      "print 'test-statistics: %s, one-tail p-value: %s'%(testStat,pVal/2.)# this p value is for 2 tail so we divide by two to get one tail\n",
      "\n",
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.xticks(fontsize=14)  \n",
      "plt.yticks(fontsize=14)\n",
      "xlabel = \"Largest issuer's proportion of plans with premium increase minus \\nmarket share weighted average proportion for other issuers in the same state (%)\"\n",
      "plt.xlabel(xlabel, fontsize=16)\n",
      "plt.ylabel(\"Frequency\", fontsize=16)\n",
      "\n",
      "diffs = map(lambda x:x*100,diffs)\n",
      "\n",
      "x0 = np.mean(diffs)\n",
      "plt.annotate('Mean: {:0.1f}'.format(x0), xy=(x0, 1), xytext=(-15, 15),\n",
      "        xycoords=('data', 'axes fraction'), textcoords='offset points',\n",
      "        horizontalalignment='left', verticalalignment='center',\n",
      "        arrowprops=dict(arrowstyle='-|>', fc='black', shrinkA=0, shrinkB=0,\n",
      "                        connectionstyle='angle,angleA=0,angleB=90,rad=10'),fontsize=14\n",
      "        )\n",
      "\n",
      "plt.axvline(x0, color='r', linestyle='dashed', linewidth=2)\n",
      "y,x,_ = plt.hist(diffs,bins=10,color=\"#3F5D7D\")\n",
      "plt.savefig(\"figures/fig7.png\", bbox_inches=\"tight\");\n",
      "\n",
      "# cache data in csv form\n",
      "pd.DataFrame(zip(x,y), columns=[xlabel,\"Frequency\"])\\\n",
      "    .set_index(xlabel).to_csv('figures/fig 7 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean: 0.189047056579\n",
        "test-statistics: 2.58395114683, one-tail p-value: 0.00829742111945\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DgDv+dtuUsdxx+61DikaSpNkpMkf3+ciIuD9wMbBjZv5vZ/h7gJdm5sM6w0Z3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\nch9c57MPpSeW3YCPAE/sxbyA0uXhrsA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       "text": [
        "<matplotlib.figure.Figure at 0x10a9f10d0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To the extent experience period data is available, there is no evidence that dominant player face a higher medical loss ratio that others."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fig 8"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diffs = []\n",
      "MLR1 = []\n",
      "MLR2 = []\n",
      "for s,g in df[(df.ClaimsExp15>1)&(df.PremiumsExp15>1)].groupby('State'):\n",
      "    if 1 in g.MarketRank.tolist() and len(g.MarketRank)>1:\n",
      "        t = g[g.MarketRank>=2]\n",
      "        if t.empty:continue\n",
      "        diffs.append(g[g.MarketRank==1].MLR.irow(0)-sum(t.MarketSize/sum(t.MarketSize)*t.MLR))\n",
      "        MLR1.append(g[g.MarketRank==1].MLR.irow(0))\n",
      "        MLR2.append(sum(t.MarketSize/sum(t.MarketSize)*t.MLR))\n",
      "\n",
      "# print statistical test results\n",
      "print 'Mean: %s'%np.mean(diffs)\n",
      "testStat,pVal = stats.ttest_1samp(diffs,0) \n",
      "print 'test-statistics: %s, one-tail p-value: %s'%(testStat,pVal/2.)# this p value is for 2 tail so we divide by two to get one tail        \n",
      "        \n",
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.xticks(fontsize=14)  \n",
      "plt.yticks(fontsize=14)\n",
      "xlabel = \"Ratio of incurred claims to premiums in 2014 for largest issuer \\nminus market weighted average ratio for other issuers in the same state\"\n",
      "plt.xlabel(xlabel, fontsize=16)\n",
      "plt.ylabel(\"Frequency\", fontsize=16)\n",
      "\n",
      "\n",
      "x0 = np.mean(diffs)\n",
      "plt.annotate('Mean: {:0.3f}'.format(x0), xy=(x0, 1), xytext=(-15, 15),\n",
      "        xycoords=('data', 'axes fraction'), textcoords='offset points',\n",
      "        horizontalalignment='left', verticalalignment='center',\n",
      "        arrowprops=dict(arrowstyle='-|>', fc='black', shrinkA=0, shrinkB=0,\n",
      "                        connectionstyle='angle,angleA=0,angleB=90,rad=10'),fontsize=14\n",
      "        )\n",
      "\n",
      "plt.axvline(x0, color='r', linestyle='dashed', linewidth=2)\n",
      "y,x,_ = plt.hist(diffs,bins=10,color=\"#3F5D7D\")\n",
      "plt.savefig(\"figures/fig8.png\", bbox_inches=\"tight\");\n",
      "\n",
      "# cache data in csv form\n",
      "pd.DataFrame(zip(x,y), columns=[xlabel,\"Frequency\"])\\\n",
      "    .set_index(xlabel).to_csv('figures/fig 8 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean: 0.0210123308098\n",
        "test-statistics: 0.501394145072, one-tail p-value: 0.310926766437\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BlwEbAkcDR0bE9hMcaz+NpVy2BH4GbEUum6uBP0XEsyc41n4ZS5k8Cbie/OjB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a8bafd0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Figure 9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# diagram on number of issuers in each state\n",
      "plt.figure(figsize=(10, 7))\n",
      "ax = plt.axes()\n",
      "ax.spines[\"top\"].set_visible(False)  \n",
      "ax.spines[\"right\"].set_visible(False) \n",
      "ax.get_xaxis().tick_bottom()  \n",
      "ax.get_yaxis().tick_left()  \n",
      "plt.yticks(fontsize=14)  \n",
      "plt.xlabel(\"\", fontsize=16)\n",
      "plt.ylabel(\"Ratio of incurred claims to premium in 2014\", fontsize=16)\n",
      "x = ['Largest issuers in states','Other issuers']\n",
      "y = [np.mean(MLR1),np.mean(MLR2)]\n",
      "plt.bar(range(len(x)),y,color=\"#3F5D7D\")\n",
      "plt.xticks(map(lambda x:x+.4,range(len(x))), x, fontsize=16)\n",
      "# plt.hist(reduce(lambda x,y:x+y,[v.values() for v in marketLookup.values()]),bins = 20,color=\"#3F5D7D\")\n",
      "# plt.title(\"Number of Issuers in each State\", fontsize=16)\n",
      "plt.savefig(\"figures/fig9.png\", bbox_inches=\"tight\");\n",
      "print np.mean(MLR1),np.mean(MLR2)\n",
      "\n",
      "# data\n",
      "pd.DataFrame(zip(x,y),columns=['Issuer Type',\"Ratio of incurred claims to premium in 2014\"]).set_index('Issuer Type').to_csv('figures/fig 9 - data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.853556017296 0.832543686486\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pEqnqrVBVS7scqKqWJDkX2B44sadoO5qBB13No2nVW4Mm4bvbok23nsVhJEmS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\nfQNPZqWqbgLOGnJIXazoIBg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       "text": [
        "<matplotlib.figure.Figure at 0x10a746290>"
       ]
      }
     ],
     "prompt_number": 27
    }
   ],
   "metadata": {}
  }
 ]
}